• 지구물리
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• 제9권2호
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• pp.99-103
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• 2006

3D object recognition independent of translation and rotation using an ultrasonic sensor array, invariant moment vectors and SOFM(Self Organizing Feature Map) neural networks is presented. Using invariant moment vectors of the acquired 16×8 pixel data of square, rectangular, cylindric and regular triangular blocks, 3D objects could be classified by SOFM neural networks. Invariant moment vectors are constant independent of translation and rotation. The recognition rates for the training and testing data were 95.91% and 92.13%, respectively.

• 대한전기학회논문지:전력기술부문A
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• 제48권1호
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• pp.76-81
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• 1999

The degree of relative similarity between 2D patterns is obtained using Open-Ball Scheme. Open-Ball Scheme employs a method of transforming the geometrical information on 3D objects or 2D patterns into the features to measure the relative similarity for object(patten) recognition, with invariance on scale, rotation, and translation. The feature of an object is used to obtain the relative similarity and mapped into [0, 1] the interval of real line. For decades, Moment-Invariant Method has been used as one of the excellent methods for pattern classification and object recognition. Open-Ball Scheme uses the geometrical structure of patterns while Moment Invariant Method uses the statistical characteristics. Open-Ball Scheme is compared to Moment Invariant Method with respect to the way that it interprets two-dimensional patten classification, especially the paradigms are compared by the degree of closeness to human's intuitive understanding. Finally the effectiveness of the proposed Open-Ball Scheme is illustrated through simulations.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2001년도 춘계학술대회 논문집(한국공작기계학회)
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• pp.131-136
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• 2001

The purpose of this paper is to develop image inspection system endows an automatic operating and measuring that the moment values are invariant with respect to variable object size and rotation. In this paper, using these moment feature vector with Hu s 7 invariant moment is also given. The characteristics of section which is applied in the mechanics used moment descriptor of invariant moment detection algorithm for image inspection system. Corresponding rates between 94% and 96% have been achived for all object tested.

• International Journal of Control, Automation, and Systems
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• 제6권6호
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• pp.800-808
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• 2008

A fingerprint verification system based on a set of invariant moment features and a nonlinear Back Propagation Neural Network(BPNN) verifier is proposed. An image-based method with invariant moment features for fingerprint verification is used to overcome the demerits of traditional minutiae-based methods and other image-based methods. The proposed system contains two stages: an off-line stage for template processing and an on-line stage for testing with input fingerprints. The system preprocesses fingerprints and reliably detects a unique reference point to determine a Region-of-Interest(ROI). A total of four sets of seven invariant moment features are extracted from four partitioned sub-images of an ROI. Matching between the feature vectors of a test fingerprint and those of a template fingerprint in the database is evaluated by a nonlinear BPNN and its performance is compared with other methods in terms of absolute distance as a similarity measure. The experimental results show that the proposed method with BPNN matching has a higher matching accuracy, while the method with absolute distance has a faster matching speed. Comparison results with other famous methods also show that the proposed method outperforms them in verification accuracy.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2449-2451
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• 2001

The purpose of this paper is to develop image inspection system endows an automatic operating and measuring that the moment values are invariant with respect to variable object size and rotation. In this paper, using these moment feature vector with Hu's 7 invariant moment is also given. The characteristics of section which is applied in the mechanics used moment descriptor of invariant moment detection algorithm for image inspection system. Corresponding rates between 94% and 96% have archived for all object tested.

• 한국정보기술학회논문지
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• 제17권5호
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• pp.13-20
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• 2019

최근 CNN을 기반으로 한 객체 검출 기술의 연구가 활발하다. 객체 검출 기술은 자율주행차, 지능형 영상분석 등에서 중요한 기술로 사용된다. 본 논문에서는 CNN 기반의 객체 검출기 중 하나인 SSD(Single Shot Multibox Detector)에 MI-FL(Moment Invariant-Feature Layer)을 적용하여 회전 변형에 강인한 객체 검출 시스템을 제안한다. 먼저 VGG 네트워크를 기반으로 입력 이미지의 특징을 추출한다. 그 후 총 6개의 특징 계층(Feature layer)을 적용하여 객체의 위치 정보와 종류를 예측해 경계 박스들을 생성한다. 그 후 NMS 알고리즘을 이용해 가장 객체일 확률이 높은 경계 박스를 얻는다. 하나의 객체 경계 박스가 정해지면 MI-FL을 이용해 해당 영역의 불변 모멘트 특징을 추출하여 미리 저장하고 학습한다. 이후 검출 과정에서 미리 저장해둔 불면모멘트 특징 정보를 이용해 검출함으로써 회전된 이미지에 대해 기존 방법보다 더 강인한 검출이 가능하다. 기존의 SSD와 MI-FL을 적용한 SSD의 비교를 통해 약 4~5%의 성능 향상을 확인하였다.

• 제어로봇시스템학회논문지
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• 제9권2호
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• pp.131-139
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• 2003

Shape descriptors play an important role in systems for object recognition, retrieval, registration, and analysis. Seven well-known descriptors including MPEG-7 visual descriptors arebriefly reviewed and a new robust pattern recognition descriptor is proposed. Performance comparison among descriptors are presented. Experiments show that the newly proposed descriptor yields better performance results than Fourier, invariant moment, and edge histogram descriptors.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.1237-1239
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• 1996

3-D object recognition and restoration independent of the translation and rotation using an ultrasonic sensor array, neural networks and invariant moment are presented. Using invariant moment vectors on the acquired $16{\times}8$ pixel data, 3-D objects can be classified by SOFM(Self Organizing Feature Map) neural networks. Invariant moment vectors kept constant independent of the translation and rotation. The experiment result shows the suggested method can be applied to the environment recognition.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2330-2332
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• 2000

In this study, 3-D underwater object recognition using ultrasonic sensor fabricated with PZT-Polymer 1-3 type composites and invariant moment vector and SOFM(Self Organizing Feature Map) neural networks are presented. The recognition rates for the training data and the testing data were 99% and 93%, respectively.

• 한국군사과학기술학회지
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• 제3권2호
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• pp.88-100
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• 2000

The design of an automatic aircraft recognition system involves two parts. The first part is extraction of invariant features independent of scale, rotation and translation. The second part is determination of optimal decision procedures, which are needed in the classification process. In this research, we extracted invariant aircraft features regardless of size, rotation and translation using Fourier Descriptors and Zernike Moments and classified using neural networks.